Kernel Author:
Bhishan Poudel, Data Scientist, Ph.D Astrophysics .
Bhishan Poudel, Data Scientist, Ph.D Astrophysics .
Data Description¶
This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.
Imports¶
In [1]:
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import os
import time
# random state
random_state=100
np.random.seed(random_state) # we need this in each cell
np.random.set_state=random_state
# Jupyter notebook settings for pandas
pd.set_option('display.max_columns', 50)
pd.set_option('display.float_format', '{:,.2g}'.format) # numbers sep by comma
pd.set_option('display.max_rows', 20) # None for all the rows
pd.set_option('display.max_colwidth', 50)
import IPython
from IPython.display import display
print([(x.__name__,x.__version__) for x in [np, pd,sns,matplotlib]])
In [2]:
%load_ext autoreload
%autoreload 2
In [3]:
%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;
In [4]:
import bokeh
from bokeh.io import output_file, output_notebook
from bokeh.plotting import figure, show, reset_output
from bokeh.models import ColumnDataSource
from bokeh.layouts import row, column, gridplot
from bokeh.models.widgets import Tabs, Panel
from bokeh.palettes import Spectral6
from bokeh.models import ColumnDataSource,FactorRange
# Output the visualization directly in the notebook
output_notebook()
[(x.__name__,x.__version__) for x in [bokeh]]
Out[4]:
In [5]:
%%javascript
IPython.OutputArea.auto_scroll_threshold = 9999;
Useful Scripts¶
In [6]:
def show_method_attributes(method, ncols=7):
""" Show all the attributes of a given method.
Example:
========
show_method_attributes(list)
"""
x = [i for i in dir(method) if i[0]!='_']
x = [i for i in x if i not in 'os np pd sys time psycopg2'.split()]
return pd.DataFrame(np.array_split(x,ncols)).T.fillna('')
Load the data¶
In [7]:
df = pd.read_csv('../data/processed/data_cleaned_encoded.csv')
print(df.shape)
df.head().T
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Univariate Analysis¶
Discrete variables and Categorical variables¶
In [8]:
df['bedrooms'].value_counts()
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In [9]:
from bhishan.util_bokeh import countplot_bokeh
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ofile = '../reports/bokeh_outputs/bedrooms_countplot.html'
countplot_bokeh(df, 'bedrooms',height=400,ofile=None)
In [11]:
countplot_bokeh(df,'view',height=300)
Continuous variables¶
histograms¶
In [12]:
from bhishan.util_bokeh import histogram_bokeh
histogram_bokeh(df,'sqft_living',n_bins=20)
Bi-variate Analysis¶
Scatter plots¶
In [13]:
from bhishan.util_bokeh import scatterplot_bokeh
In [14]:
ofile = '../reports/bokeh_outputs/sqftLiving_vs_price.html'
scatterplot_bokeh(df,'sqft_living','price',ofile=ofile)
Multi-variate Analysis¶
countplots¶
In [15]:
from bhishan.util_bokeh import stacked_countplot_bokeh
stacked_countplot_bokeh(df,'bedrooms','yr_sales','price')
Map visualization¶
In [16]:
df[['lat','long']].head()
Out[16]:
In [17]:
from bhishan.util_bokeh import map_plot_bokeh
In [18]:
ofile = '../reports/bokeh_outputs/map.html'
map_plot_bokeh(df, 'lat', 'long',ofile)